This document has code embedded throughout. In the next section we will create a visualization using the already loaded dataset cryptodata:
datatable(cryptodata, rownames = FALSE,
options(list(lengthMenu = c(4, 5, 6))))
Overview
Price Chart
Interactive Chart
Python Code Example
import pandas as pd # Create the Python object from R df = r.cryptodata # Show the new Python dataframe df
## pair symbol ask_1_price date_time_utc ## 0 ETHUSD ETH 547.572 2020-12-09 05:00:01 ## 1 ETHUSD ETH 552.120 2020-12-09 04:00:01 ## 2 ETHUSD ETH 549.790 2020-12-09 03:00:01 ## 3 ETHUSD ETH 547.455 2020-12-09 02:00:01 ## 4 ETHUSD ETH 550.456 2020-12-09 01:00:01 ## ... ... ... ... ... ## 2023 ETHUSD ETH 347.606 2020-09-09 14:00:38 ## 2024 ETHUSD ETH 346.886 2020-09-09 13:00:39 ## 2025 ETHUSD ETH 347.578 2020-09-09 12:00:39 ## 2026 ETHUSD ETH 346.624 2020-09-09 11:00:38 ## 2027 ETHUSD ETH 357.844 NaT ## ## [2028 rows x 4 columns]
One more Python example
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import numpy as np
# Create a new field based on the ask_1_price value:
df['price_percentile'] = np.where(df['ask_1_price'] > np.percentile(df['ask_1_price'], 50),
'upper 50th percentile of prices',
'lower 50th percentile of prices')
# Show modified dataframe:
df[['symbol', 'ask_1_price', 'price_percentile']]
## symbol ask_1_price price_percentile ## 0 ETH 547.572 upper 50th percentile of prices ## 1 ETH 552.120 upper 50th percentile of prices ## 2 ETH 549.790 upper 50th percentile of prices ## 3 ETH 547.455 upper 50th percentile of prices ## 4 ETH 550.456 upper 50th percentile of prices ## ... ... ... ... ## 2023 ETH 347.606 lower 50th percentile of prices ## 2024 ETH 346.886 lower 50th percentile of prices ## 2025 ETH 347.578 lower 50th percentile of prices ## 2026 ETH 346.624 lower 50th percentile of prices ## 2027 ETH 357.844 lower 50th percentile of prices ## ## [2028 rows x 3 columns]
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